35 research outputs found
Mathematical optimization techniques for resource allocation in cognitive radio networks
Introduction of data intensive multimedia and interactive services together with exponential growth of wireless applications have created a spectrum crisis. Many spectrum occupancy measurements, however, have shown that most of the allocated spectrum are used inefficiently indicating that radically new approaches are required for better utilization of spectrum. This motivates the concept of opportunistic spectrum sharing or the so-called cognitive radio technology that has great potential to improve spectrum utilization. This technology allows the secondary users to access the spectrum which is allocated to the licensed users in order to transmit their own signal without harmfully affecting the licensed users' communications. In this thesis, an optimal radio resource allocation algorithm is proposed for an OFDM based underlay cognitive radio networks. The proposed algorithm optimally allocates transmission power and OFDM subchannels to the users at the basestation in order to satisfy the quality of services and interference leakage constraints based on integer linear programming. To reduce the computational complexity, a novel recursive suboptimal algorithm is proposed based on a linear optimization framework. To exploit the spatial diversity, the proposed algorithms are extended to a MIMO-OFDM based cognitive radio network. Finally, a novel spatial multiplexing technique is developed to allocate resources in a cognitive radio network which consists of both the real time and the non-real users. Conditions required for convergence of the proposed algorithm are analytically derived. The performance of all these new algorithms are verified using MATLAB simulation results.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Efficient privacy-preserving facial expression classification
This paper proposes an efficient algorithm to perform privacy-preserving (PP) facial expression classification (FEC) in the client-server model. The server holds a database and offers the classification service to the clients. The client uses the service to classify the facial expression (FaE) of subject. It should be noted that the client and server are mutually untrusted parties and they want to perform the classification without revealing their inputs to each other. In contrast to the existing works, which rely on computationally expensive cryptographic operations, this paper proposes a lightweight algorithm based on the randomization technique. The proposed algorithm is validated using the widely used JAFFE and MUG FaE databases. Experimental results demonstrate that the proposed algorithm does not degrade the performance compared to existing works. However, it preserves the privacy of inputs while improving the computational complexity by 120 times and communication complexity by 31 percent against the existing homomorphic cryptography based approach
FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users
The federated learning (FL) technique was initially developed to mitigate
data privacy issues that can arise in the traditional machine learning
paradigm. While FL ensures that a user's data always remain with the user, the
gradients of the locally trained models must be communicated with the
centralized server to build the global model. This results in privacy leakage,
where the server can infer private information of the users' data from the
shared gradients. To mitigate this flaw, the next-generation FL architectures
proposed encryption and anonymization techniques to protect the model updates
from the server. However, this approach creates other challenges, such as a
malicious user might sabotage the global model by sharing false gradients.
Since the gradients are encrypted, the server is unable to identify and
eliminate rogue users which would protect the global model. Therefore, to
mitigate both attacks, this paper proposes a novel fully homomorphic encryption
(FHE) based scheme suitable for FL. We modify the one-to-one single-key
Cheon-Kim-Kim-Song (CKKS)-based FHE scheme into a distributed multi-key
additive homomorphic encryption scheme that supports model aggregation in FL.
We employ a novel aggregation scheme within the encrypted domain, utilizing
users' non-poisoning rates, to effectively address data poisoning attacks while
ensuring privacy is preserved by the proposed encryption scheme. Rigorous
security, privacy, convergence, and experimental analyses have been provided to
show that FheFL is novel, secure, and private, and achieves comparable accuracy
at reasonable computational cost
Robust MMSE beamforming for multiantenna relay networks
In this paper, we propose a robust minimum mean square error (MMSE) based beamforming
technique for multiantenna relay broadcast channels, where a multi-antenna base station transmits signal to single antenna users with the help of a multiantenna relay. The signal transmission from the base station to the single antenna users is completed in two time slots, where the relay receives the signal from the base station in the first time slot and it then forwards the received signal to different users based on amplify and forward protocol. We propose a robust beamforming technique for sum-power
minimization problem with imperfect channel state information (CSI) between the relay and the users. This robust scheme is developed based on the worst-case optimization framework and Nemirovski
Lemma by incorporating uncertainties in the CSI. The original optimization problem is divided into three subproblems due to joint non-convexity in terms of beamforming vectors at the base station, the relay amplification matrix, and receiver coefficients. These subproblems are formulated into a convex optimization framework by exploiting Nemirovski Lemma, and an iterative algorithm is developed by
alternatively optimizing each of them with channel uncertainties. In addition, we provide an optimization framework to evaluate the achievable worst-case mean square error (MSE) of each user for a given set of design parameters. Simulation results have been provided to validate the convergence of the proposed algorithm
Trusted UAV Network Coverage using Blockchain, Machine Learning and Auction Mechanisms
The UAV is emerging as one of the greatest technology developments for rapid network
coverage provisioning at affordable cost. The aim of this paper is to outsource network coverage of a specific
area according to a desired quality of service requirement and to enable various entities in the network to
have intelligence to make autonomous decisions using blockchain and auction mechanisms. In this regard,
by considering a multiple-UAV network where each UAV is associated to its own controlling operator,
this paper addresses two major challenges: the selection of the UAV for the desired quality of network
coverage and the development of a distributed and autonomous real-time monitoring framework for the
enforcement of service level agreement (SLA). For a suitable UAV selection, we employ a reputation-based
auction mechanism to model the interaction between the business agent who is interested in outsourcing
the network coverage and the UAV operators serving in closeby areas. In addition, theoretical analysis
is performed to show that the proposed auction mechanism attains a dominant strategy equilibrium. For
the SLA enforcement and trust model, we propose a permissioned blockchain architecture considering
Support Vector Machine (SVM) for real-time autonomous and distributed monitoring of UAV service. In
particular, smart contract features of the blockchain are invoked for enforcing the SLA terms of payment
and penalty, and for quantifying the UAV service reputation. Simulation results confirm the accuracy of
theoretical analysis and efficacy of the proposed model
User collusion avoidance scheme for privacy-preserving decentralized key-policy attribute-based encryption
Decentralized attribute-based encryption (ABE) is a variant of multi-authority based ABE whereby any attribute authority (AA) can independently join and leave the system without collaborating with the existing AAs. In this paper, we propose a user collusion avoidance scheme which preserves the user's privacy when they interact with multiple authorities to obtain decryption credentials. The proposed scheme mitigates the well-known user collusion security vulnerability found in previous schemes. We show that our scheme relies on the standard complexity assumption (decisional bilienar Deffie-Hellman assumption). This is contrast to previous schemes which relies on non-standard assumption (q-decisional Diffie-Hellman inversion)
Privacy-preserving iVector-based speaker verification
This work introduces an efficient algorithm to
develop a privacy-preserving (PP) voice verification based on
iVector and linear discriminant analysis techniques. This research
considers a scenario in which users enrol their voice biometric
to access different services (i.e., banking). Once enrolment is
completed, users can verify themselves using their voice-print
instead of alphanumeric passwords. Since a voice-print is unique
for everyone, storing it with a third-party server raises several
privacy concerns. To address this challenge, this work proposes
a novel technique based on randomisation to carry out voice authentication,
which allows the user to enrol and verify their voice
in the randomised domain. To achieve this, the iVector based
voice verification technique has been redesigned to work on the
randomised domain. The proposed algorithm is validated using
a well known speech dataset. The proposed algorithm neither
compromises the authentication accuracy nor adds additional
complexity due to the randomisation operations
Privacy-preserving clinical decision support system using gaussian kernel-based classification
A clinical decision support system forms a critical capability to link health observations with health knowledge to influence choices by clinicians for improved healthcare. Recent trends toward remote outsourcing can be exploited to provide efficient and accurate clinical decision support in healthcare. In this scenario, clinicians can use the health knowledge located in remote servers via the Internet to diagnose their patients. However, the fact that these servers are third party and therefore potentially not fully trusted raises possible privacy concerns. In this paper, we propose a novel privacy-preserving protocol for a clinical decision support system where the patients' data always remain in an encrypted form during the diagnosis process. Hence, the server involved in the diagnosis process is not able to learn any extra knowledge about the patient's data and results. Our experimental results on popular medical datasets from UCI-database demonstrate that the accuracy of the proposed protocol is up to 97.21% and the privacy of patient data is not compromised